A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy

Divya Jhinjharia, Aman Chandra Kaushik, Shakti Sahi

Research output: Contribution to journalArticlepeer-review

Abstract

Diabetes mellitus is a metabolic disorder that persists as a global threat to the world. A G-protein coupled receptor (GPCR), free fatty acid receptor 4 (FFAR4), has emerged as a potential target for type 2 diabetes mellitus (T2DM) and obesity-related disorders. The current study has investigated the FFAR4, deploying 3-dimensional structure modeling, molecular docking, machine learning, and high-throughput virtual screening methods to unravel the receptor’s crucial and non-crucial binding site residues. We screened four lakh compounds and shortlisted them based on binding energy, stereochemical considerations, non-bonded interactions, and pharmacokinetic profiling. Out of the screened compounds, four compounds were selected for ligand-bound simulations. The molecular dynamic simulations were carried out for 1µs for native FFAR4 and 500 ns each for complexes of FFAR4 with compound 1, compound 2, compound 3, and compound 4. Our findings showed that in addition to reported binding site residues ARG99, ARG183, and VAL98 in known agonists like TUG-891, the amino acids ARG22, ARG24, THR23, TRP305, and GLU43 were also critical binding site residues. These amino acids impart stability to the FFAR4 complexes and contribute to the stronger binding affinity of the compounds. The study also indicated that aromatic residues like PHE211 are crucial for recognizing the active site’s pi-pi and C-C double bonds. Since FFAR4 is a membrane protein, the simulation studies give an insight into the mechanisms of the crucial protein-lipid and lipid-water interactions. The analysis of the molecular dynamics trajectories showed all four compounds as potential hit molecules that can be developed further into potential agonists for T2DM therapy. Amongst the four compounds, compound 4 showed relatively better binding affinity, stronger non-bonded interactions, and a stable complex. Communicated by Ramaswamy H. Sarma.

Original languageEnglish
JournalJournal of Biomolecular Structure and Dynamics
DOIs
StateAccepted/In press - 1 Jan 2023
Externally publishedYes

Keywords

  • FFAR4
  • HTVS
  • diabetes
  • machine-learning
  • molecular dynamic simulations

ASJC Scopus subject areas

  • Molecular Biology
  • Structural Biology

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